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DOC: Change "Convention for examples" to use Series name as ser instead of s #53304

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DOC: Change "Convention for examples" to use Series name as ser instead of s #53304

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20 changes: 10 additions & 10 deletions doc/source/development/contributing_docstring.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -695,10 +695,10 @@ and avoiding aliases. Avoid excessive imports, but if needed, imports from
the standard library go first, followed by third-party libraries (like
matplotlib).

When illustrating examples with a single ``Series`` use the name ``s``, and if
When illustrating examples with a single ``Series`` use the name ``ser``, and if
illustrating with a single ``DataFrame`` use the name ``df``. For indices,
``idx`` is the preferred name. If a set of homogeneous ``Series`` or
``DataFrame`` is used, name them ``s1``, ``s2``, ``s3``... or ``df1``,
``DataFrame`` is used, name them ``ser1``, ``ser2``, ``ser3``... or ``df1``,
``df2``, ``df3``... If the data is not homogeneous, and more than one structure
is needed, name them with something meaningful, for example ``df_main`` and
``df_to_join``.
Expand Down Expand Up @@ -731,8 +731,8 @@ positional arguments ``head(3)``.

Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s.mean()
>>> ser = pd.Series([1, 2, 3])
>>> ser.mean()
2
"""
pass
Expand All @@ -744,8 +744,8 @@ positional arguments ``head(3)``.

Examples
--------
>>> s = pd.Series([1, np.nan, 3])
>>> s.fillna(0)
>>> ser = pd.Series([1, np.nan, 3])
>>> ser.fillna(0)
[1, 0, 3]
"""
pass
Expand All @@ -756,10 +756,10 @@ positional arguments ``head(3)``.

Examples
--------
>>> s = pd.Series([380., 370., 24., 26],
>>> ser = pd.Series([380., 370., 24., 26],
... name='max_speed',
... index=['falcon', 'falcon', 'parrot', 'parrot'])
>>> s.groupby_mean()
>>> ser.groupby_mean()
index
falcon 375.0
parrot 25.0
Expand All @@ -776,8 +776,8 @@ positional arguments ``head(3)``.

Examples
--------
>>> s = pd.Series('Antelope', 'Lion', 'Zebra', np.nan)
>>> s.contains(pattern='a')
>>> ser = pd.Series('Antelope', 'Lion', 'Zebra', np.nan)
>>> ser.contains(pattern='a')
0 False
1 False
2 True
Expand Down